if (!require("remotes"))
install.packages("remotes")
remotes::install_github("flavjack/inti")ORGANIC FERTILIZERS IN THE QUALITY AND POST-HARVEST MANAGEMENT OF MANGO (Mangifera indica L). VAR. KENT
1 Setup
Install development version.
library(emmeans)
library(corrplot)
library(multcomp)
library(FSA)
library(factoextra)
library(corrplot)
library(png)
source('https://inkaverse.com/setup.r')
session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.1 (2024-06-14 ucrt)
os Windows 11 x64 (build 22631)
system x86_64, mingw32
ui RTerm
language (EN)
collate Spanish_Peru.utf8
ctype Spanish_Peru.utf8
tz America/Lima
date 2024-08-27
pandoc 3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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AlgDesign 1.2.1 2022-05-25 [1] CRAN (R 4.4.0)
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[1] C:/Users/INIA/AppData/Local/R/win-library/4.4
[2] C:/Program Files/R/R-4.4.1/library
──────────────────────────────────────────────────────────────────────────────2 Import data
Data from the variables evaluated in 2023 during the 2022-2023 growing season. The evaluations focused on mango fruits of the ‘Kent’ variety at two stages: physiological maturity and commercial maturity.
url <- "https://docs.google.com/spreadsheets/d/1cjWrS-EVcII85c-l_NuEfTpjhVMI156e8REM9GDVP_w/edit?gid=95386135#gid=95386135"
gs <- url %>%
as_sheets_id()
tratamiento <- gs %>%
range_read("tratamientos") %>%
rename_with(~ tolower(.))
rendimiento <- gs %>%
range_read("rendimiento") %>%
rename_with(~ tolower(.))
fisio <- gs %>%
range_read("fisio") %>%
rename_with(~ tolower(.)) %>%
merge(., tratamiento) %>%
dplyr::select(tratamiento,compost, biol,everything()) %>%
merge(., rendimiento) %>%
mutate(across(tratamiento:nfrutos, ~ as.factor(.))) %>%
rename(treat = tratamiento
, repetition = repeticion
, composts = compost)
str(fisio)
## 'data.frame': 405 obs. of 15 variables:
## $ treat : Factor w/ 9 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ repetition: Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ nplantas : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 2 2 2 2 2 ...
## $ composts : Factor w/ 3 levels "0","5","15": 1 1 1 1 1 1 1 1 1 1 ...
## $ biol : Factor w/ 3 levels "0","5","10": 1 1 1 1 1 1 1 1 1 1 ...
## $ nfrutos : Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ...
## $ pcfmf : num 90 70 60 40 70 70 40 60 30 80 ...
## $ ffmf : num 13.2 12 10 10.2 12.8 11.6 10.8 11.9 11 11.2 ...
## $ cifmf : num 2 2 2 2 2 1.5 2 2 2.5 2.5 ...
## $ ssfmf : num 8.8 8.6 8.5 8.2 8.6 9.8 9.7 9.4 9.2 8.5 ...
## $ phfmf : num 2.6 2.55 2.52 2.58 2.55 ...
## $ atfmf : num 1.39 1.38 1.2 1.12 1.35 1.49 1.29 1.52 1.54 1.42 ...
## $ msfmf : num 19.1 19.1 19.1 19.1 19.1 ...
## $ imf : num 6.33 6.23 7.08 7.32 6.37 6.58 7.52 6.18 5.97 5.99 ...
## $ rpp : num 51.3 51.3 51.3 51.3 51.3 52.4 52.4 52.4 52.4 52.4 ...
consumo <- gs %>%
range_read("consumo") %>%
rename_with(~ tolower(.)) %>%
merge(., tratamiento) %>%
dplyr::select(tratamiento,compost, biol,everything()) %>%
mutate(across(tratamiento:nfrutos, ~ as.factor(.))) %>%
rename(treat = tratamiento
, repetition = repeticion
, composts = compost
, n_fruits = nfrutos)
glimpse(consumo)
## Rows: 135
## Columns: 12
## $ treat <fct> T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0,…
## $ composts <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5,…
## $ biol <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ repetition <fct> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1,…
## $ n_fruits <fct> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5,…
## $ pdfmc <dbl> 6.68, 6.78, 7.02, 6.78, 6.12, 6.62, 6.91, 7.04, 6.45, 6.50,…
## $ ffmc <dbl> 3.0, 3.0, 4.0, 3.8, 4.2, 4.0, 3.6, 3.0, 3.0, 3.0, 3.0, 3.4,…
## $ cifmc <dbl> 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.5, 3.0, 3.0, 3.0, 3.5, 3.0,…
## $ ssfmc <dbl> 14.6, 15.6, 14.8, 15.0, 14.0, 14.4, 15.5, 15.1, 15.2, 15.2,…
## $ phfmc <dbl> 4.22, 4.19, 4.21, 4.15, 4.26, 4.34, 4.35, 4.34, 4.30, 4.38,…
## $ atfmc <dbl> 0.600, 0.700, 0.700, 0.500, 0.500, 0.605, 0.615, 0.625, 0.6…
## $ imf <dbl> 24.33333, 22.28571, 21.14286, 30.00000, 28.00000, 23.80165,…3 Data summary
Summary of the number of data points recorded for each treatment and evaluated variable.
sm <- fisio %>%
group_by(treat) %>%
summarise(across(pcfmf:rpp, ~ sum(!is.na(.))))
sm
## # A tibble: 9 × 10
## treat pcfmf ffmf cifmf ssfmf phfmf atfmf msfmf imf rpp
## <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 T0 45 45 45 45 45 45 45 45 45
## 2 T1 45 45 45 45 45 45 45 45 45
## 3 T2 45 45 45 45 45 45 45 45 45
## 4 T3 45 45 45 45 45 45 45 45 45
## 5 T4 45 45 45 45 45 45 45 45 45
## 6 T5 45 45 45 45 45 45 45 45 45
## 7 T6 45 45 45 45 45 45 45 45 45
## 8 T7 45 45 45 45 45 45 45 45 45
## 9 T8 45 45 45 45 45 45 45 45 45
sm <- consumo %>%
group_by(treat) %>%
summarise(across(pdfmc:imf, ~ sum(!is.na(.))))
sm
## # A tibble: 9 × 8
## treat pdfmc ffmc cifmc ssfmc phfmc atfmc imf
## <fct> <int> <int> <int> <int> <int> <int> <int>
## 1 T0 15 15 15 15 15 15 15
## 2 T1 15 15 15 15 15 15 15
## 3 T2 15 15 15 15 15 15 15
## 4 T3 15 15 15 15 15 15 15
## 5 T4 15 15 15 15 15 15 15
## 6 T5 15 15 15 15 15 15 15
## 7 T6 15 15 15 15 15 15 15
## 8 T7 15 15 15 15 15 15 15
## 9 T8 15 15 15 15 15 15 154 Objetives
The objective of this study is to demonstrate the effect of organic fertilizers, specifically compost and biol, applied at the soil and foliar levels on the quality of mango fruit at physiological and commercial maturity.
4.1 Specific Objective 1
Demonstrate the effect of organic fertilizers, specifically compost and biol, applied at the soil and foliar levels on the quality of mango fruit at physiological maturity.
4.1.1 Fruit firmness at physiological maturity (FFPM)
trait <- "ffmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol ffmf resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: ffmf
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 29.954 14.9771 19.6376 0.0000000073854003 ***
## biol 2 48.734 24.3670 31.9495 0.0000000000001369 ***
## composts:biol 4 1.737 0.4344 0.5695 0.6849
## Residuals 396 302.018 0.7627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group |
|---|---|---|---|---|---|---|---|
| 10 | 0 | 12.22000 | 0.1301854 | 396 | 11.96406 | 12.47594 | a |
| 5 | 0 | 11.80444 | 0.1301854 | 396 | 11.54850 | 12.06039 | ab |
| 0 | 0 | 11.38667 | 0.1301854 | 396 | 11.13073 | 11.64261 | b |
| 10 | 5 | 12.20444 | 0.1301854 | 396 | 11.94850 | 12.46039 | a |
| 5 | 5 | 11.94667 | 0.1301854 | 396 | 11.69073 | 12.20261 | a |
| 0 | 5 | 11.49333 | 0.1301854 | 396 | 11.23739 | 11.74927 | b |
| 10 | 15 | 12.96000 | 0.1301854 | 396 | 12.70406 | 13.21594 | a |
| 5 | 15 | 12.33111 | 0.1301854 | 396 | 12.07517 | 12.58705 | b |
| 0 | 15 | 11.95556 | 0.1301854 | 396 | 11.69961 | 12.21150 | b |
p1a <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "Fruit firmness at physiological maturity (kgf.cm^{-2})"
, glab = "Biol"
, ylimits = c(0, 16, 4)
)
p1a
4.1.2 Internal fruit color at physiological maturity (IFCPM)
trait <- "cifmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index repetition composts biol cifmf resi res_MAD
## 6 6 1 0 0 1.5 -0.6599549 -13.354004
## 9 9 1 0 0 2.5 0.3400451 6.880719
## 10 10 1 0 0 2.5 0.3400451 6.880719
## 11 11 1 0 0 2.5 0.3400451 6.880719
## 18 18 2 0 0 2.5 0.3400451 6.880719
## 21 21 2 0 0 2.5 0.3400451 6.880719
## 23 23 2 0 0 2.5 0.3400451 6.880719
## 24 24 2 0 0 2.5 0.3400451 6.880719
## 25 25 2 0 0 2.5 0.3400451 6.880719
## 26 26 2 0 0 2.5 0.3400451 6.880719
## 27 27 2 0 0 2.5 0.3400451 6.880719
## 28 28 2 0 0 2.5 0.3400451 6.880719
## 34 34 3 0 0 1.5 -0.6800902 -13.761438
## 36 36 3 0 0 3.0 0.8199098 16.590647
## 37 37 3 0 0 2.5 0.3199098 6.473285
## 38 38 3 0 0 2.5 0.3199098 6.473285
## 40 40 3 0 0 3.0 0.8199098 16.590647
## 52 52 1 5 0 2.5 0.3733785 7.555210
## 54 54 1 5 0 2.5 0.3733785 7.555210
## 55 55 1 5 0 2.5 0.3733785 7.555210
## 67 67 2 5 0 1.5 -0.6266215 -12.679513
## 68 68 2 5 0 2.5 0.3733785 7.555210
## 69 69 2 5 0 1.0 -1.1266215 -22.796875
## 70 70 2 5 0 2.5 0.3733785 7.555210
## 72 72 2 5 0 2.5 0.3733785 7.555210
## 76 76 3 5 0 2.5 0.3532431 7.147776
## 81 81 3 5 0 2.5 0.3532431 7.147776
## 83 83 3 5 0 2.5 0.3532431 7.147776
## 84 84 3 5 0 2.5 0.3532431 7.147776
## 85 85 3 5 0 2.5 0.3532431 7.147776
## 86 86 3 5 0 2.5 0.3532431 7.147776
## 87 87 3 5 0 2.5 0.3532431 7.147776
## 89 89 3 5 0 2.5 0.3532431 7.147776
## 90 90 3 5 0 2.5 0.3532431 7.147776
## 94 94 1 15 0 2.5 0.3733785 7.555210
## 96 96 1 15 0 2.5 0.3733785 7.555210
## 103 103 1 15 0 2.5 0.3733785 7.555210
## 104 104 1 15 0 2.5 0.3733785 7.555210
## 105 105 1 15 0 2.5 0.3733785 7.555210
## 108 108 2 15 0 2.5 0.3733785 7.555210
## 111 111 2 15 0 1.5 -0.6266215 -12.679513
## 116 116 2 15 0 2.5 0.3733785 7.555210
## 119 119 2 15 0 2.5 0.3733785 7.555210
## 124 124 3 15 0 2.5 0.3532431 7.147776
## 126 126 3 15 0 2.5 0.3532431 7.147776
## 129 129 3 15 0 2.5 0.3532431 7.147776
## 134 134 3 15 0 2.5 0.3532431 7.147776
## 135 135 3 15 0 2.5 0.3532431 7.147776
## 141 141 1 0 5 2.5 0.3400451 6.880719
## 142 142 1 0 5 2.5 0.3400451 6.880719
## 144 144 1 0 5 2.5 0.3400451 6.880719
## 146 146 1 0 5 2.5 0.3400451 6.880719
## 147 147 1 0 5 2.5 0.3400451 6.880719
## 156 156 2 0 5 2.5 0.3400451 6.880719
## 158 158 2 0 5 2.5 0.3400451 6.880719
## 160 160 2 0 5 2.5 0.3400451 6.880719
## 162 162 2 0 5 2.5 0.3400451 6.880719
## 163 163 2 0 5 2.5 0.3400451 6.880719
## 171 171 3 0 5 3.0 0.8199098 16.590647
## 175 175 3 0 5 2.5 0.3199098 6.473285
## 176 176 3 0 5 2.5 0.3199098 6.473285
## 177 177 3 0 5 2.5 0.3199098 6.473285
## 183 183 1 0 10 2.5 0.3733785 7.555210
## 185 185 1 0 10 2.5 0.3733785 7.555210
## 186 186 1 0 10 2.5 0.3733785 7.555210
## 189 189 1 0 10 2.5 0.3733785 7.555210
## 191 191 1 0 10 2.5 0.3733785 7.555210
## 192 192 1 0 10 2.5 0.3733785 7.555210
## 195 195 1 0 10 2.5 0.3733785 7.555210
## 204 204 2 0 10 2.5 0.3733785 7.555210
## 206 206 2 0 10 2.5 0.3733785 7.555210
## 208 208 2 0 10 1.5 -0.6266215 -12.679513
## 213 213 3 0 10 2.5 0.3532431 7.147776
## 223 223 3 0 10 3.0 0.8532431 17.265137
## 225 225 3 0 10 2.5 0.3532431 7.147776
## 231 231 1 5 5 2.5 0.3733785 7.555210
## 234 234 1 5 5 2.5 0.3733785 7.555210
## 235 235 1 5 5 2.5 0.3733785 7.555210
## 236 236 1 5 5 2.5 0.3733785 7.555210
## 243 243 2 5 5 2.5 0.3733785 7.555210
## 244 244 2 5 5 2.5 0.3733785 7.555210
## 246 246 2 5 5 2.5 0.3733785 7.555210
## 248 248 2 5 5 2.5 0.3733785 7.555210
## 250 250 2 5 5 2.5 0.3733785 7.555210
## 253 253 2 5 5 2.5 0.3733785 7.555210
## 255 255 2 5 5 2.5 0.3733785 7.555210
## 257 257 3 5 5 2.5 0.3532431 7.147776
## 269 269 3 5 5 1.5 -0.6467569 -13.086947
## 270 270 3 5 5 2.5 0.3532431 7.147776
## 281 281 1 5 10 2.5 0.3956007 8.004870
## 282 282 1 5 10 2.5 0.3956007 8.004870
## 285 285 1 5 10 2.5 0.3956007 8.004870
## 296 296 2 5 10 2.5 0.3956007 8.004870
## 298 298 2 5 10 2.5 0.3956007 8.004870
## 300 300 2 5 10 2.5 0.3956007 8.004870
## 301 301 3 5 10 2.5 0.3754653 7.597437
## 305 305 3 5 10 2.5 0.3754653 7.597437
## 310 310 3 5 10 2.5 0.3754653 7.597437
## 311 311 3 5 10 2.5 0.3754653 7.597437
## 316 316 1 15 5 2.5 0.3511562 7.105549
## 321 321 1 15 5 2.5 0.3511562 7.105549
## 325 325 1 15 5 2.5 0.3511562 7.105549
## 329 329 1 15 5 2.5 0.3511562 7.105549
## 332 332 2 15 5 2.5 0.3511562 7.105549
## 336 336 2 15 5 2.5 0.3511562 7.105549
## 338 338 2 15 5 2.5 0.3511562 7.105549
## 342 342 2 15 5 2.5 0.3511562 7.105549
## 346 346 3 15 5 3.0 0.8310209 16.815477
## 348 348 3 15 5 3.0 0.8310209 16.815477
## 354 354 3 15 5 2.5 0.3310209 6.698116
## 358 358 3 15 5 2.5 0.3310209 6.698116
## 362 362 1 15 10 2.0 -0.4821771 -9.756720
## 368 368 1 15 10 2.0 -0.4821771 -9.756720
## 374 374 1 15 10 2.0 -0.4821771 -9.756720
## 375 375 1 15 10 2.0 -0.4821771 -9.756720
## 379 379 2 15 10 3.0 0.5178229 10.478003
## 387 387 2 15 10 2.0 -0.4821771 -9.756720
## 388 388 2 15 10 2.0 -0.4821771 -9.756720
## 391 391 3 15 10 3.0 0.4976875 10.070569
## 393 393 3 15 10 3.0 0.4976875 10.070569
## 395 395 3 15 10 3.0 0.4976875 10.070569
## 398 398 3 15 10 3.0 0.4976875 10.070569
## 399 399 3 15 10 2.0 -0.5023125 -10.164153
## 402 402 3 15 10 2.0 -0.5023125 -10.164153
## 403 403 3 15 10 3.0 0.4976875 10.070569
## 405 405 3 15 10 3.0 0.4976875 10.070569
## rawp.BHStud adjp bholm
## 6 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 9 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 10 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 11 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 18 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 21 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 23 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 24 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 25 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 26 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 27 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 28 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 34 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 36 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 37 0.000000000095894625574 0.000000000095894625574 0.0000000272340736629
## 38 0.000000000095894625574 0.000000000095894625574 0.0000000272340736629
## 40 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 52 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 54 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 55 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 67 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
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## 69 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 70 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 72 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 76 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 81 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 83 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 84 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 85 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 86 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 87 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 89 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 90 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 94 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 96 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 103 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 104 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 105 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 108 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 111 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 116 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 119 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 124 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 126 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 129 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 134 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 135 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 141 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 142 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 144 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 146 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 147 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 156 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 158 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 160 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 162 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 163 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 171 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 175 0.000000000095894625574 0.000000000095894625574 0.0000000272340736629
## 176 0.000000000095894625574 0.000000000095894625574 0.0000000272340736629
## 177 0.000000000095894625574 0.000000000095894625574 0.0000000272340736629
## 183 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 185 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 186 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 189 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 191 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 192 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 195 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 204 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 206 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 208 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 213 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 223 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 225 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 231 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 234 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 235 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 236 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 243 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 244 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 246 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 248 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 250 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 253 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 255 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 257 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 269 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 270 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 281 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 282 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 285 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 296 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 298 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 300 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 301 0.000000000000030198066 0.000000000000030198066 0.0000000000112034826
## 305 0.000000000000030198066 0.000000000000030198066 0.0000000000112034826
## 310 0.000000000000030198066 0.000000000000030198066 0.0000000000112034826
## 311 0.000000000000030198066 0.000000000000030198066 0.0000000000112034826
## 316 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 321 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 325 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 329 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 332 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 336 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 338 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 342 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 346 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 348 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 354 0.000000000021112445125 0.000000000021112445125 0.0000000060381593059
## 358 0.000000000021112445125 0.000000000021112445125 0.0000000060381593059
## 362 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 368 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 374 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 375 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 379 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 387 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 388 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 391 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 393 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 395 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 398 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 399 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 402 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 403 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 405 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## out_flag
## 6 OUTLIER
## 9 OUTLIER
## 10 OUTLIER
## 11 OUTLIER
## 18 OUTLIER
## 21 OUTLIER
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## 255 OUTLIER
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## 269 OUTLIER
## 270 OUTLIER
## 281 OUTLIER
## 282 OUTLIER
## 285 OUTLIER
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## 300 OUTLIER
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## 321 OUTLIER
## 325 OUTLIER
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## 338 OUTLIER
## 342 OUTLIER
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## 354 OUTLIER
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## 362 OUTLIER
## 368 OUTLIER
## 374 OUTLIER
## 375 OUTLIER
## 379 OUTLIER
## 387 OUTLIER
## 388 OUTLIER
## 391 OUTLIER
## 393 OUTLIER
## 395 OUTLIER
## 398 OUTLIER
## 399 OUTLIER
## 402 OUTLIER
## 403 OUTLIER
## 405 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: cifmf
## Df Sum Sq Mean Sq F value
## composts 2 1.5872 0.79358 13798771145988843021802240660
## biol 2 1.6520 0.82599 14362199539619202704044242464
## composts:biol 4 3.4544 0.86360 15016274787197207858626608666
## Residuals 270 0.0000 0.00000
## Pr(>F)
## composts < 0.00000000000000022 ***
## biol < 0.00000000000000022 ***
## composts:biol < 0.00000000000000022 ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 1 | 5 | 0 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 3 | 10 | 0 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 2 | 0 | 0 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 4 | 10 | 5 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 5 | 5 | 5 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 6 | 0 | 5 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 7 | 10 | 15 | 2.5 | 0 | 270 | 2.5 | 2.5 | a |
| 8 | 5 | 15 | 2.0 | 0 | 270 | 2.0 | 2.0 | b |
| 9 | 0 | 15 | 2.0 | 0 | 270 | 2.0 | 2.0 | b |
p1b <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "Internal fruit color at physiological maturity"
, ylimits = c(0, 3, 1)
)
p1b
4.1.3 Fruit pH at physiological maturity (FpHPM)
trait <- "phfmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index repetition composts biol phfmf resi res_MAD rawp.BHStud
## 390 390 2 15 10 3.507795 0.7353291 3.98034 0.0000688167
## adjp bholm out_flag
## 390 0.0000688167 0.02787076 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: phfmf
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 0.8058 0.40290 12.1492 0.00000757832 ***
## biol 2 1.2013 0.60065 18.1126 0.00000002978 ***
## composts:biol 4 0.5173 0.12932 3.8996 0.004051 **
## Residuals 395 13.0991 0.03316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | 10 | 0 | 2.711098 | 0.0271466 | 395 | 2.657728 | 2.764468 | a |
| 1 | 0 | 0 | 2.594716 | 0.0271466 | 395 | 2.541346 | 2.648086 | b |
| 3 | 5 | 0 | 2.526739 | 0.0271466 | 395 | 2.473369 | 2.580109 | b |
| 4 | 10 | 5 | 2.737604 | 0.0271466 | 395 | 2.684234 | 2.790974 | a |
| 5 | 5 | 5 | 2.634018 | 0.0271466 | 395 | 2.580648 | 2.687388 | b |
| 6 | 0 | 5 | 2.540791 | 0.0271466 | 395 | 2.487421 | 2.594161 | c |
| 7 | 10 | 15 | 2.745579 | 0.0274534 | 395 | 2.691606 | 2.799552 | a |
| 8 | 5 | 15 | 2.710735 | 0.0271466 | 395 | 2.657365 | 2.764104 | a |
| 9 | 0 | 15 | 2.692818 | 0.0271466 | 395 | 2.639448 | 2.746188 | a |
p1c <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "pH del Fruto"
, ylimits = c(0, 4, 1)
)
p1c
4.1.4 Soluble solids content of the fruit at physiological maturity (SSCFPM)
trait <- "ssfmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index repetition composts biol ssfmf resi res_MAD rawp.BHStud
## 390 390 2 15 10 12.1 2.787154 4.673809 0.000002956642
## adjp bholm out_flag
## 390 0.000002956642 0.00119744 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: ssfmf
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 10.124 5.0620 13.9979 0.000001338318 ***
## biol 2 14.392 7.1959 19.8987 0.000000005838 ***
## composts:biol 4 4.753 1.1882 3.2858 0.01146 *
## Residuals 395 142.842 0.3616
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 1 | 10 | 0 | 9.194444 | 0.0896445 | 395 | 9.018204 | 9.370684 | a |
| 2 | 5 | 0 | 8.767778 | 0.0896445 | 395 | 8.591538 | 8.944018 | b |
| 3 | 0 | 0 | 8.574444 | 0.0896445 | 395 | 8.398205 | 8.750684 | b |
| 4 | 10 | 5 | 9.291111 | 0.0896445 | 395 | 9.114871 | 9.467351 | a |
| 5 | 5 | 5 | 8.913333 | 0.0896445 | 395 | 8.737093 | 9.089573 | b |
| 6 | 0 | 5 | 8.646667 | 0.0896445 | 395 | 8.470427 | 8.822907 | b |
| 7 | 5 | 15 | 9.293333 | 0.0896445 | 395 | 9.117093 | 9.469573 | a |
| 9 | 10 | 15 | 9.244318 | 0.0906575 | 395 | 9.066087 | 9.422550 | a |
| 8 | 0 | 15 | 9.127778 | 0.0896445 | 395 | 8.951538 | 9.304018 | a |
p1d <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "Soluble solids content of the fruit at physiological maturity (brix^{o})"
, ylimits = c(0, 12, 2)
)
p1d
4.1.5 Titratable acidity of the fruit at physiological maturity (TAFPM)
trait <- "atfmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol atfmf resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: atfmf
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 0.19915 0.099574 15.5979 0.00000030165 ***
## biol 2 0.23570 0.117851 18.4609 0.00000002161 ***
## composts:biol 4 0.08017 0.020043 3.1397 0.01465 *
## Residuals 396 2.52799 0.006384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 3 | 0 | 0 | 1.365333 | 0.0119106 | 396 | 1.341917 | 1.388749 | a |
| 2 | 5 | 0 | 1.313556 | 0.0119106 | 396 | 1.290140 | 1.336971 | b |
| 1 | 10 | 0 | 1.260444 | 0.0119106 | 396 | 1.237029 | 1.283860 | c |
| 6 | 5 | 5 | 1.301556 | 0.0119106 | 396 | 1.278140 | 1.324971 | a |
| 4 | 0 | 5 | 1.298000 | 0.0119106 | 396 | 1.274584 | 1.321416 | a |
| 5 | 10 | 5 | 1.270889 | 0.0119106 | 396 | 1.247473 | 1.294305 | a |
| 9 | 0 | 15 | 1.278000 | 0.0119106 | 396 | 1.254584 | 1.301416 | a |
| 8 | 5 | 15 | 1.263889 | 0.0119106 | 396 | 1.240473 | 1.287305 | ab |
| 7 | 10 | 15 | 1.235111 | 0.0119106 | 396 | 1.211695 | 1.258527 | b |
p1e <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "Titratable acidity of the fruit at physiological maturity ('%')"
, ylimits = c(0, 2, 1)
)
p1e
4.1.6 Fruit dry matter percentage at physiological maturity (FDMPPM)
trait <- "msfmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol msfmf resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: msfmf
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 45.336 22.668 30.4208 0.0000000000005129 ***
## biol 2 72.171 36.085 48.4270 < 0.00000000000000022 ***
## composts:biol 4 6.782 1.695 2.2753 0.06058 .
## Residuals 396 295.079 0.745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group |
|---|---|---|---|---|---|---|---|
| 10 | 0 | 20.02778 | 0.1286813 | 396 | 19.77479 | 20.28076 | a |
| 5 | 0 | 19.31778 | 0.1286813 | 396 | 19.06479 | 19.57076 | b |
| 0 | 0 | 19.26889 | 0.1286813 | 396 | 19.01590 | 19.52187 | b |
| 10 | 5 | 20.00222 | 0.1286813 | 396 | 19.74924 | 20.25521 | a |
| 5 | 5 | 19.55744 | 0.1286813 | 396 | 19.30446 | 19.81043 | b |
| 0 | 5 | 19.07333 | 0.1286813 | 396 | 18.82035 | 19.32632 | c |
| 10 | 15 | 20.96133 | 0.1286813 | 396 | 20.70835 | 21.21432 | a |
| 5 | 15 | 20.21600 | 0.1286813 | 396 | 19.96302 | 20.46898 | b |
| 0 | 15 | 19.57556 | 0.1286813 | 396 | 19.32257 | 19.82854 | c |
p1f <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "Fruit dry matter at physiological maturity ('%')"
, ylimits = c(0, 25, 5)
)
p1f
4.1.7 Figure 1
Univariate analysis of the most critical variables for determining the physiological maturity of the fruit at harvest time and for preventing handling damage during commercialization or industrial processes.
legend <- cowplot::get_plot_component(p1a, 'guide-box-top', return_all = TRUE)
p1 <- list(p1a + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
# , p1b + labs(x = NULL) + theme(legend.position="none"
# , axis.title.x=element_blank()
# , axis.text.x=element_blank()
# , axis.ticks.x=element_blank())
# , p1c + labs(x = NULL) + theme(legend.position="none"
# , axis.title.x=element_blank()
# , axis.text.x=element_blank()
# , axis.ticks.x=element_blank())
, p1d + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p1e + theme(legend.position="none")
, p1f + theme(legend.position="none")
) %>%
plot_grid(plotlist = ., ncol = 2
, labels = "auto"
)
fig <- plot_grid(legend, p1, ncol = 1, align = 'v', rel_heights = c(0.05, 1))
fig %>%
ggsave2(plot = ., "files/Fig-1.jpg"
, units = "cm"
, width = 25
, height = 27
)
fig %>%
ggsave2(plot = ., "files/Fig-1.eps"
, units = "cm"
, width = 25
, height = 27
)
knitr::include_graphics("files/Fig-1.jpg")
4.1.8 Multivariate analysis
Principal Component Analysis (PCA) of quality characteristics to correlate with mango fruits at physiological maturity from compost and biol applications.
mv <- fisio %>%
group_by(composts, biol) %>%
summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%
unite("treat", composts:biol, sep = "-") %>%
rename(Treat = treat
,PFCCPM = pcfmf
,FFPM = ffmf
,IFCPM = cifmf
,SSCFPM = ssfmf
,FpHPM = phfmf
,TAFPM = atfmf
,FDMPPM = msfmf
,FPMI = imf
,RPP = rpp)
pca <- mv %>%
PCA(scale.unit = T, quali.sup = 1, graph = F)
# summary
summary(pca, nbelements = Inf, nb.dec = 2)
##
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7 Dim.8
## Variance 7.14 0.92 0.52 0.30 0.10 0.02 0.01 0.00
## % of var. 79.29 10.27 5.74 3.28 1.11 0.21 0.10 0.01
## Cumulative % of var. 79.29 89.56 95.30 98.58 99.69 99.89 99.99 100.00
##
## Individuals
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2
## 1 | 4.39 | -4.14 26.70 0.89 | 0.91 9.94 0.04 | -0.02 0.01 0.00 |
## 2 | 2.85 | -2.46 9.45 0.75 | 0.60 4.36 0.04 | -0.94 18.96 0.11 |
## 3 | 1.94 | 1.03 1.66 0.28 | -0.67 5.42 0.12 | -1.41 42.81 0.53 |
## 4 | 3.09 | -2.82 12.40 0.83 | 0.06 0.04 0.00 | 0.87 16.47 0.08 |
## 5 | 0.98 | -0.79 0.98 0.66 | -0.20 0.47 0.04 | 0.32 2.18 0.11 |
## 6 | 2.04 | 1.64 4.19 0.65 | -1.13 15.46 0.31 | -0.20 0.89 0.01 |
## 7 | 1.35 | 0.50 0.38 0.14 | -0.88 9.25 0.42 | 0.85 15.42 0.39 |
## 8 | 2.36 | 2.19 7.47 0.86 | -0.71 6.07 0.09 | 0.34 2.43 0.02 |
## 9 | 5.27 | 4.86 36.77 0.85 | 2.02 48.98 0.15 | 0.20 0.83 0.00 |
##
## Variables
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2
## PFCCPM | 0.96 12.91 0.92 | -0.04 0.18 0.00 | -0.11 2.41 0.01 |
## FFPM | 0.97 13.27 0.95 | 0.17 3.23 0.03 | -0.12 2.91 0.02 |
## IFCPM | 0.57 4.59 0.33 | 0.81 71.60 0.66 | 0.07 0.95 0.00 |
## SSCFPM | 0.95 12.55 0.90 | -0.29 9.06 0.08 | -0.10 1.89 0.01 |
## FpHPM | 0.90 11.35 0.81 | -0.17 3.25 0.03 | -0.02 0.06 0.00 |
## TAFPM | -0.93 12.00 0.86 | 0.13 1.88 0.02 | 0.01 0.03 0.00 |
## FDMPPM | 0.95 12.62 0.90 | 0.23 5.63 0.05 | -0.12 2.84 0.01 |
## FPMI | 0.97 13.20 0.94 | -0.20 4.29 0.04 | -0.07 1.05 0.01 |
## RPP | 0.73 7.51 0.54 | -0.09 0.88 0.01 | 0.67 87.86 0.45 |
##
## Supplementary categories
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test Dim.3 cos2 v.test
## 0-0 | 4.39 | -4.14 0.89 -1.55 | 0.91 0.04 0.95 | -0.02 0.00 -0.03 |
## 0-10 | 1.94 | 1.03 0.28 0.39 | -0.67 0.12 -0.70 | -1.41 0.53 -1.96 |
## 0-5 | 2.85 | -2.46 0.75 -0.92 | 0.60 0.04 0.63 | -0.94 0.11 -1.31 |
## 15-0 | 1.35 | 0.50 0.14 0.19 | -0.88 0.42 -0.91 | 0.85 0.39 1.18 |
## 15-10 | 5.27 | 4.86 0.85 1.82 | 2.02 0.15 2.10 | 0.20 0.00 0.27 |
## 15-5 | 2.36 | 2.19 0.86 0.82 | -0.71 0.09 -0.74 | 0.34 0.02 0.47 |
## 5-0 | 3.09 | -2.82 0.83 -1.06 | 0.06 0.00 0.06 | 0.87 0.08 1.22 |
## 5-10 | 2.04 | 1.64 0.65 0.61 | -1.13 0.31 -1.18 | -0.20 0.01 -0.28 |
## 5-5 | 0.98 | -0.79 0.66 -0.30 | -0.20 0.04 -0.21 | 0.32 0.11 0.44 |
f2a <- plot.PCA(x = pca, choix = "var"
, cex=0.8
)
f2b <- plot.PCA(x = pca, choix = "ind"
, habillage = 1
, invisible = c("ind")
, cex=0.8
, ylim = c(-3,3)
) 4.1.9 Figure 2
Principal Component Analysis (PCA).
fig <- list(f2a, f2b) %>%
plot_grid(plotlist = ., ncol = 2, nrow = 1
, labels = "auto"
, rel_widths = c(1, 1.5)
)
fig %>%
ggsave2(plot = ., "files/Fig-2.jpg", units = "cm"
, width = 25
, height = 10
)
fig %>%
ggsave2(plot = ., "files/Fig-2.eps", units = "cm"
, width = 25
, height = 10
)
knitr::include_graphics("files/Fig-2.jpg")
4.1.10 Supplementary Figure 1
Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).
var <- get_pca_var(pca)
tmp <- tempfile(fileext = ".png")
ppi <- 300
png(tmp, width=8*ppi, height=10*ppi, res=ppi)
corrplot(var$cor,
method="number",
tl.col="black",
tl.srt=45,)
graphics.off()
pt1 <- png::readPNG(tmp) %>%
grid::rasterGrob(interpolate = TRUE)
pt2 <- fviz_eig(pca,
addlabels=TRUE,
hjust = 0.05,
barfill="white",
barcolor ="darkblue",
linecolor ="red") +
ylim(0, 90) +
labs(
title = "PCA - percentage of explained variances",
y = "Variance (%)") +
theme_minimal()
pt3 <- fviz_contrib(pca,
choice = "var",
axes = 1,
top = 10,
fill="white",
color ="darkblue",
sort.val = "desc") +
ylim(0, 15) +
labs(title = "Dim 1 - variables contribution")
pt4 <- fviz_contrib(pca,
choice = "var",
axes = 2,
top = 10,
fill="white",
color ="darkblue",
sort.val = "desc") +
ylim(0, 80) +
labs(title = "Dim 2 - variables contribution")
plot <- ggdraw(xlim = c(0.0, 1.0), ylim = c(0, 1.0))+
draw_plot(pt1, width = 0.4, height = 0.99, x = 0.62, y = 0.0) +
draw_plot(pt2, width = 0.6, height = 0.34, x = 0.03, y = 0.66) +
draw_plot(pt3, width = 0.6, height = 0.34, x = 0.03, y = 0.33) +
draw_plot(pt4, width = 0.6, height = 0.34, x = 0.03, y = 0.0) +
draw_plot_label(
label = c("a", "b", "c", "d"),
x = c(0.005, 0.005, 0.005, 0.65),
y = c(0.999, 0.67, 0.34, 0.999))
ggsave2(plot = plot, "files/FigS1.jpg", height = 25, width = 40, units = "cm")
ggsave2(plot = plot, "files/FigS1.eps", height = 25, width = 40, units = "cm")
knitr::include_graphics("files/FigS1.jpg")
4.2 Specific Objective 2
Demonstrate the effect of organic fertilizers, specifically compost and biol, applied at the soil and foliar levels on the quality of mango fruit at commercial maturity.
4.2.1 Fruit firmness at commercial maturity (FFCM)
trait <- "ffmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol ffmc resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: ffmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 6.6441 3.3221 25.4488 0.000000000521 ***
## biol 2 2.7739 1.3870 10.6248 0.000054440296 ***
## composts:biol 4 1.4536 0.3634 2.7839 0.02947 *
## Residuals 126 16.4480 0.1305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | 10 | 0 | 4.053333 | 0.093288 | 126 | 3.868719 | 4.237947 | a |
| 1 | 0 | 0 | 3.546667 | 0.093288 | 126 | 3.362052 | 3.731281 | b |
| 3 | 5 | 0 | 3.440000 | 0.093288 | 126 | 3.255386 | 3.624614 | b |
| 4 | 10 | 5 | 4.093333 | 0.093288 | 126 | 3.908719 | 4.277947 | a |
| 5 | 5 | 5 | 4.040000 | 0.093288 | 126 | 3.855386 | 4.224614 | a |
| 6 | 0 | 5 | 3.813333 | 0.093288 | 126 | 3.628719 | 3.997947 | a |
| 7 | 10 | 15 | 4.333333 | 0.093288 | 126 | 4.148719 | 4.517948 | a |
| 8 | 5 | 15 | 4.213333 | 0.093288 | 126 | 4.028719 | 4.397947 | a |
| 9 | 0 | 15 | 4.120000 | 0.093288 | 126 | 3.935386 | 4.304614 | a |
p2a <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "Fruit firmness at commercial maturity (kgf.cm^{-2})"
, glab = "Biol"
, ylimits = c(0, 6, 2)
)
p2a
4.2.2 Soluble solids content of the fruit at commercial maturity (SSCFCM)
trait <- "ssfmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol ssfmc resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: ssfmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 13.192 6.5961 19.7094 0.00000003569 ***
## biol 2 6.876 3.4379 10.2725 0.00007365188 ***
## composts:biol 4 0.332 0.0830 0.2479 0.9105
## Residuals 126 42.168 0.3347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group |
|---|---|---|---|---|---|---|---|
| 10 | 0 | 15.44000 | 0.149369 | 126 | 15.14440 | 15.73560 | a |
| 5 | 0 | 15.04667 | 0.149369 | 126 | 14.75107 | 15.34226 | ab |
| 0 | 0 | 14.92667 | 0.149369 | 126 | 14.63107 | 15.22226 | b |
| 10 | 5 | 15.46667 | 0.149369 | 126 | 15.17107 | 15.76226 | a |
| 5 | 5 | 15.22000 | 0.149369 | 126 | 14.92440 | 15.51560 | ab |
| 0 | 5 | 14.84667 | 0.149369 | 126 | 14.55107 | 15.14226 | b |
| 10 | 15 | 16.12000 | 0.149369 | 126 | 15.82440 | 16.41560 | a |
| 5 | 15 | 15.72667 | 0.149369 | 126 | 15.43107 | 16.02226 | ab |
| 0 | 15 | 15.61333 | 0.149369 | 126 | 15.31774 | 15.90893 | b |
p2b <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "Soluble solids content of the fruit at commercial maturity (brix^{o})"
, ylimits = c(0, 18, 3)
)
p2b
4.2.3 Titratable acidity of the fruit at commercial maturity (TAFCM)
trait <- "atfmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index repetition composts biol atfmc resi res_MAD rawp.BHStud
## 4 4 1 0 0 0.50 -0.1157152 -4.533406 0.00000580400
## 5 5 1 0 0 0.50 -0.1157152 -4.533406 0.00000580400
## 18 18 1 5 0 0.74 0.1062848 4.163951 0.00003127878
## 27 27 3 5 0 0.50 -0.1006342 -3.942576 0.00008061124
## adjp bholm out_flag
## 4 0.00000580400 0.0007835399 OUTLIER
## 5 0.00000580400 0.0007835399 OUTLIER
## 18 0.00003127878 0.0041600772 OUTLIER
## 27 0.00008061124 0.0106406836 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: atfmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 0.219750 0.109875 94.7899 < 0.00000000000000022 ***
## biol 2 0.054531 0.027266 23.5222 0.00000000229 ***
## composts:biol 4 0.023460 0.005865 5.0598 0.0008311 ***
## Residuals 122 0.141416 0.001159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | 0 | 0 | 0.6169231 | 0.0094427 | 122 | 0.5982303 | 0.6356159 | a |
| 3 | 10 | 0 | 0.5946667 | 0.0087907 | 122 | 0.5772646 | 0.6120687 | a |
| 1 | 5 | 0 | 0.5940000 | 0.0087907 | 122 | 0.5765979 | 0.6114021 | a |
| 6 | 0 | 5 | 0.6192308 | 0.0094427 | 122 | 0.6005380 | 0.6379236 | a |
| 5 | 5 | 5 | 0.5496667 | 0.0087907 | 122 | 0.5322646 | 0.5670687 | b |
| 4 | 10 | 5 | 0.5346667 | 0.0087907 | 122 | 0.5172646 | 0.5520687 | b |
| 9 | 0 | 15 | 0.5193333 | 0.0087907 | 122 | 0.5019313 | 0.5367354 | a |
| 8 | 5 | 15 | 0.5138667 | 0.0087907 | 122 | 0.4964646 | 0.5312687 | a |
| 7 | 10 | 15 | 0.4746667 | 0.0087907 | 122 | 0.4572646 | 0.4920687 | b |
p2c <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "Titratable acidity of the fruit at commercial maturity ('%')"
, ylimits = c(0, 0.8, 0.2)
)
p2c
4.2.4 Fruit dehydration percentage at commercial maturity (FDPCM)
trait <- "pdfmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol pdfmc resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: pdfmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 2.9454 1.47269 28.9546 0.00000000004501 ***
## biol 2 1.6263 0.81316 15.9875 0.00000064906170 ***
## composts:biol 4 0.2066 0.05164 1.0153 0.4022
## Residuals 126 6.4086 0.05086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 3 | 0 | 0 | 6.760667 | 0.0582307 | 126 | 6.645430 | 6.875903 | a |
| 2 | 5 | 0 | 6.539333 | 0.0582307 | 126 | 6.424097 | 6.654570 | b |
| 1 | 10 | 0 | 6.408000 | 0.0582307 | 126 | 6.292763 | 6.523237 | b |
| 6 | 0 | 5 | 6.685333 | 0.0582307 | 126 | 6.570097 | 6.800570 | a |
| 5 | 5 | 5 | 6.492000 | 0.0582307 | 126 | 6.376763 | 6.607237 | ab |
| 4 | 10 | 5 | 6.375333 | 0.0582307 | 126 | 6.260097 | 6.490570 | b |
| 9 | 0 | 15 | 6.301333 | 0.0582307 | 126 | 6.186096 | 6.416570 | a |
| 8 | 5 | 15 | 6.236000 | 0.0582307 | 126 | 6.120763 | 6.351237 | a |
| 7 | 10 | 15 | 6.162667 | 0.0582307 | 126 | 6.047430 | 6.277903 | a |
p2d <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "Fruit dehydration at commercial maturity ('%')"
, glab = "Biol"
, ylimits = c(0, 8, 2)
)
p2d
4.2.5 Fruit pH at commercial maturity (FpHCM)
trait <- "phfmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index repetition composts biol phfmc resi res_MAD rawp.BHStud
## 27 27 3 5 0 3.8 -0.4029042 -4.421673 0.00000979398
## adjp bholm out_flag
## 27 0.00000979398 0.001322187 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: phfmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 1.51062 0.75531 54.8131 < 0.00000000000000022 ***
## biol 2 0.64152 0.32076 23.2776 0.00000000255 ***
## composts:biol 4 0.26493 0.06623 4.8065 0.001221 **
## Residuals 125 1.72246 0.01378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group |
|---|---|---|---|---|---|---|---|
| 10 | 0 | 4.388000 | 0.0303092 | 125 | 4.328014 | 4.447986 | a |
| 5 | 0 | 4.369333 | 0.0303092 | 125 | 4.309348 | 4.429319 | a |
| 0 | 0 | 4.332000 | 0.0303092 | 125 | 4.272014 | 4.391986 | a |
| 10 | 5 | 4.484000 | 0.0303092 | 125 | 4.424014 | 4.543986 | a |
| 5 | 5 | 4.429333 | 0.0303092 | 125 | 4.369348 | 4.489319 | a |
| 0 | 5 | 4.201429 | 0.0313730 | 125 | 4.139337 | 4.263520 | b |
| 10 | 15 | 4.692667 | 0.0303092 | 125 | 4.632681 | 4.752652 | a |
| 5 | 15 | 4.568667 | 0.0303092 | 125 | 4.508681 | 4.628652 | b |
| 0 | 15 | 4.520000 | 0.0303092 | 125 | 4.460014 | 4.579986 | b |
p2e <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "Fruit pH at commercial maturity"
, ylimits = c(0, 6, 2)
)
p2e
4.2.6 Internal fruit color at commercial maturity (IFCCM)
trait <- "cifmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol cifmc resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: cifmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 1.4890 0.74452 5.4384 0.005427 **
## biol 2 1.9690 0.98452 7.1915 0.001103 **
## composts:biol 4 2.0914 0.52285 3.8192 0.005776 **
## Residuals 126 17.2493 0.13690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group |
|---|---|---|---|---|---|---|---|
| 10 | 0 | 3.766667 | 0.0955334 | 126 | 3.577609 | 3.955725 | a |
| 5 | 0 | 3.566667 | 0.0955334 | 126 | 3.377609 | 3.755724 | a |
| 0 | 0 | 3.066667 | 0.0955334 | 126 | 2.877609 | 3.255724 | b |
| 10 | 5 | 3.633333 | 0.0955334 | 126 | 3.444275 | 3.822391 | a |
| 5 | 5 | 3.633333 | 0.0955334 | 126 | 3.444275 | 3.822391 | a |
| 0 | 5 | 3.593333 | 0.0955334 | 126 | 3.404275 | 3.782391 | a |
| 10 | 15 | 3.800000 | 0.0955334 | 126 | 3.610942 | 3.989058 | a |
| 5 | 15 | 3.700000 | 0.0955334 | 126 | 3.510942 | 3.889058 | a |
| 0 | 15 | 3.666667 | 0.0955334 | 126 | 3.477609 | 3.855724 | a |
p2f <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Composts"
, ylab = "Internal fruit color at commercial maturity"
, ylimits = c(0, 5, 1)
)
p2f
4.2.7 Figure 3
Univariate analysis of the most crucial variables for determining the commercial maturity of the fruit in the postharvest handling process.
legend <- cowplot::get_plot_component(p2a, 'guide-box-top', return_all = TRUE)
p2 <- list(p2a + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p2b + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p2c + theme(legend.position="none")
, p2d + theme(legend.position="none")
# , p2e + theme(legend.position="none")
# , p2f + theme(legend.position="none")
) %>%
plot_grid(plotlist = ., ncol = 2
, labels = "auto"
)
fig <- plot_grid(legend, p2, ncol = 1, align = 'v', rel_heights = c(0.05, 1))
fig %>%
ggsave2(plot = ., "files/Fig-3.jpg"
, units = "cm"
, width = 25
, height = 27
)
fig %>%
ggsave2(plot = ., "files/Fig-3.eps"
, units = "cm"
, width = 25
, height = 27
)
knitr::include_graphics("files/Fig-3.jpg")
4.2.8 Multivariate analysis
Principal Component Analysis (PCA) of quality characteristics to correlate with mango fruits at commercial maturity from compost and biol applications.
mv <- consumo %>%
group_by(composts, biol) %>%
summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%
unite("treat", composts:biol, sep = "-") %>%
rename(Treat = treat
,FDPCM = pdfmc
,FFCM = ffmc
,IFCCM = cifmc
,SSCFCM = ssfmc
,FpHCM = phfmc
,TAFCM = atfmc
,FCMI = imf)
pca <- mv %>%
PCA(scale.unit = T, quali.sup = 1, graph = F)
# summary
summary(pca, nbelements = Inf, nb.dec = 2)
##
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 5.94 0.72 0.22 0.08 0.03 0.01 0.00
## % of var. 84.79 10.35 3.12 1.10 0.46 0.18 0.01
## Cumulative % of var. 84.79 95.14 98.26 99.36 99.81 99.99 100.00
##
## Individuals
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2
## 1 | 3.99 | -3.53 23.36 0.78 | -1.82 50.71 0.21 | 0.30 4.70 0.01 |
## 2 | 2.48 | -2.15 8.62 0.75 | 0.02 0.01 0.00 | -1.22 76.40 0.24 |
## 3 | 1.45 | -0.25 0.11 0.03 | 1.28 25.17 0.78 | -0.03 0.05 0.00 |
## 4 | 3.26 | -3.01 16.91 0.85 | 1.12 19.22 0.12 | 0.47 11.33 0.02 |
## 5 | 0.61 | -0.15 0.04 0.06 | 0.20 0.60 0.10 | 0.33 5.43 0.28 |
## 6 | 0.89 | 0.85 1.36 0.92 | -0.05 0.04 0.00 | 0.18 1.59 0.04 |
## 7 | 1.64 | 1.62 4.92 0.98 | -0.15 0.35 0.01 | 0.02 0.03 0.00 |
## 8 | 2.31 | 2.29 9.85 0.99 | -0.11 0.19 0.00 | 0.04 0.08 0.00 |
## 9 | 4.36 | 4.31 34.81 0.98 | -0.49 3.71 0.01 | -0.09 0.39 0.00 |
##
## Variables
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2
## FDPCM | -0.98 16.06 0.95 | -0.11 1.57 0.01 | 0.14 9.00 0.02 |
## FFCM | 0.89 13.39 0.79 | 0.22 6.42 0.05 | 0.39 71.20 0.16 |
## IFCCM | 0.71 8.50 0.50 | 0.68 64.34 0.47 | -0.15 9.80 0.02 |
## SSCFCM | 0.98 16.24 0.96 | -0.06 0.49 0.00 | -0.04 0.73 0.00 |
## FpHCM | 0.94 14.82 0.88 | -0.30 12.42 0.09 | -0.14 8.48 0.02 |
## TAFCM | -0.95 15.29 0.91 | 0.25 8.69 0.06 | -0.03 0.35 0.00 |
## FCMI | 0.97 15.70 0.93 | -0.21 6.07 0.04 | 0.03 0.44 0.00 |
##
## Supplementary categories
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test Dim.3 cos2 v.test
## 0-0 | 3.99 | -3.53 0.78 -1.45 | -1.82 0.21 -2.14 | 0.30 0.01 0.65 |
## 0-10 | 1.45 | -0.25 0.03 -0.10 | 1.28 0.78 1.51 | -0.03 0.00 -0.07 |
## 0-5 | 2.48 | -2.15 0.75 -0.88 | 0.02 0.00 0.03 | -1.22 0.24 -2.62 |
## 15-0 | 1.64 | 1.62 0.98 0.67 | -0.15 0.01 -0.18 | 0.02 0.00 0.05 |
## 15-10 | 4.36 | 4.31 0.98 1.77 | -0.49 0.01 -0.58 | -0.09 0.00 -0.19 |
## 15-5 | 2.31 | 2.29 0.99 0.94 | -0.11 0.00 -0.13 | 0.04 0.00 0.08 |
## 5-0 | 3.26 | -3.01 0.85 -1.23 | 1.12 0.12 1.32 | 0.47 0.02 1.01 |
## 5-10 | 0.89 | 0.85 0.92 0.35 | -0.05 0.00 -0.06 | 0.18 0.04 0.38 |
## 5-5 | 0.61 | -0.15 0.06 -0.06 | 0.20 0.10 0.23 | 0.33 0.28 0.70 |
f4a <- plot.PCA(x = pca, choix = "var"
, cex=0.8
)
f4b <- plot.PCA(x = pca, choix = "ind"
, habillage = 1
, invisible = c("ind")
, cex=0.8
, ylim = c(-3,3)
) 4.2.9 Figure 4
Principal Component Analysis (PCA).
fig <- list(f4a, f4b) %>%
plot_grid(plotlist = ., ncol = 2, nrow = 1
, labels = "auto"
, rel_widths = c(1, 1.5)
)
fig %>%
ggsave2(plot = ., "files/Fig-4.jpg", units = "cm"
, width = 25
, height = 10
)
fig %>%
ggsave2(plot = ., "files/Fig-4.eps", units = "cm"
, width = 25
, height = 10
)
knitr::include_graphics("files/Fig-4.jpg")
4.2.10 Supplementary Figure 2
Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).
var <- get_pca_var(pca)
tmp <- tempfile(fileext = ".png")
ppi <- 300
png(tmp, width=8*ppi, height=8*ppi, res=ppi)
corrplot(var$cor,
method="number",
tl.col="black",
tl.srt=45,)
graphics.off()
pt1 <- png::readPNG(tmp) %>%
grid::rasterGrob(interpolate = TRUE)
pt2 <- fviz_eig(pca,
addlabels=TRUE,
hjust = 0.05,
barfill="white",
barcolor ="darkblue",
linecolor ="red") +
ylim(0, 100) +
labs(
title = "PCA - percentage of explained variances",
y = "Variance (%)") +
theme_minimal()
pt3 <- fviz_contrib(pca,
choice = "var",
axes = 1,
top = 10,
fill="white",
color ="darkblue",
sort.val = "desc") +
ylim(0, 20) +
labs(title = "Dim 1 - variables contribution")
pt4 <- fviz_contrib(pca,
choice = "var",
axes = 2,
top = 10,
fill="white",
color ="darkblue",
sort.val = "desc") +
ylim(0, 80) +
labs(title = "Dim 2 - variables contribution")
plot <- ggdraw(xlim = c(0.0, 1.0), ylim = c(0, 1.0))+
draw_plot(pt1, width = 0.4, height = 0.99, x = 0.62, y = 0.0) +
draw_plot(pt2, width = 0.6, height = 0.34, x = 0.03, y = 0.66) +
draw_plot(pt3, width = 0.6, height = 0.34, x = 0.03, y = 0.33) +
draw_plot(pt4, width = 0.6, height = 0.34, x = 0.03, y = 0.0) +
draw_plot_label(
label = c("a", "b", "c", "d"),
x = c(0.005, 0.005, 0.005, 0.65),
y = c(0.999, 0.67, 0.34, 0.999))
ggsave2(plot = plot, "files/FigS2.jpg", height = 25, width = 40, units = "cm")
ggsave2(plot = plot, "files/FigS2.eps", height = 25, width = 40, units = "cm")
knitr::include_graphics("files/FigS2.jpg")
5 Meteorological data
Climatic conditions of the study area located in the Tambogrande district, Piura region.
met <- range_read(ss = gs, sheet = "clima") %>%
mutate(date = as_date(Fecha))
str(met)
## tibble [180 × 7] (S3: tbl_df/tbl/data.frame)
## $ Fecha: POSIXct[1:180], format: "2022-09-01" "2022-09-02" ...
## $ TMax : num [1:180] 29 31.4 33.8 31.9 33.1 33.8 31.5 31.3 29.8 31.8 ...
## $ TMin : num [1:180] 14.8 14.1 14.8 16.2 16.8 15.6 16.1 15.8 15.4 15.4 ...
## $ D. T : num [1:180] 14.2 17.3 19 15.7 16.3 18.2 15.4 15.5 14.4 16.4 ...
## $ HR : num [1:180] 70 69.1 65.1 66.7 64.6 67.3 68.2 67.9 70.2 71.4 ...
## $ PP : num [1:180] 0 0 0 0 0 0 0 0 0 0 ...
## $ date : Date[1:180], format: "2022-09-01" "2022-09-02" ...
names(met)
## [1] "Fecha" "TMax" "TMin" "D. T" "HR" "PP" "date"
max(met$PP)
## [1] 107.9
scale <- 3
plot <- met %>%
ggplot(aes(x = date)) +
geom_line(aes(y = TMax, color = "Tmax (°C)"), size= 0.8) +
geom_line(aes(y = TMin, color = "Tmin (°C)"), size= 0.8) +
geom_bar(aes(y = PP/scale)
, stat="identity", size=.1, fill="blue", color="black", alpha=.4) +
geom_line(aes(y = HR/scale, color = "HR (%)"), size = 0.8) +
scale_color_manual("", values = c("skyblue", "red", "blue")) +
scale_y_continuous(limits = c(0, 40)
, expand = c(0, 0)
, name = "Temperature (°C)"
, sec.axis = sec_axis(~ . * scale, name = "Precipitation (mm)")
) +
scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y", name = NULL) +
theme_minimal_grid() +
theme(legend.position = "top")
plot %>%
ggsave2(plot = ., "files/weather.jpg", units = "cm"
, width = 25, height = 15)
knitr::include_graphics("files/weather.jpg")